Grounded intelligence engineered for zero hallucination, verifiable evidentiary lineage, and non-negotiable human decision sovereignty.
Algorithms exist to discover latent patterns, surface cross-standard citations, and evaluate ripple risks. Only certified human practitioners possess decision authority.
How every inference in PMX OS flows from automated discovery to permanent, auditable sign-off.
Ontological analysis detects requirement variances, unlinked hazard controls, or regulatory clause updates.
Every assertion is mathematically tied to concrete source nodes: test logs, CAD dimensions, or statutory articles.
Qualified systems engineers or clinical regulatory specialists inspect the diff, rationale, and cited lineage.
Explicit affirmative human sign-off commits the change. The machine cannot self-authorize state mutations.
The model version, prompt hash, cited evidence, reviewer identity, and timestamp are sealed into the ledger.
Engineered to eliminate the systematic failure modes of probabilistic artificial intelligence.
Standard large language models generate plausible text based on statistical token probabilities. In regulated healthcare, this produces "fluent hallucinations"—citations to non-existent clinical trials or imaginary ISO clauses.
PMX OS resolves this through Constrained Graph Generation. Model outputs must strictly resolve against existing verified entities in the customer's ontology. If a recommendation refers to a requirement, part number, or standard clause that cannot be traversed in the active graph, the assertion is rejected before rendering to the human user.
Every recommendation rendered in PMX OS displays a mathematical confidence score and an evidentiary completeness indicator (e.g., 98.4% grounded).
If the system evaluates a change impact with incomplete test coverage or ambiguous specification linkages, it does not gloss over the gap. It explicitly alerts the engineer: "Notice: Low evidence linkage on ISO 14971 Clause 7.2. Additional verification artifact required."
Machine learning models applied to medical software are subject to data distribution drift as new clinical studies, post-market adverse events, and regulatory updates emerge.
PMX OS continuously monitors inference distributions, semantic embedding stability, and precision-recall benchmarks. Any deviation triggers an automated telemetry alert for engineering inspection.
Clinical evaluation datasets and post-market safety telemetry must reflect diverse patient populations across age, biological sex, comorbidities, and demographic variables.
Our dataset validation pipelines evaluate training cohorts for statistical underrepresentation, ensuring that diagnostic and safety risk recommendations do not perpetuate historical clinical sampling biases.